Skin whitening
Picture whitening formula:
p = P*1.4(a)+ b
Code:
import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('image0.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
#cv2.imshow('src',img)
dst = np.zeros((height,width,3),np.uint8)
for i in range(0,height):
for j in range(0,width):
(b,g,r) = img[i,j]
bb = int(b*1.3) + 10
gg = int(g*1.2) + 15
if bb>255:
bb = 255
if gg>255:
gg = 255
dst[i,j] = (bb,gg,r)
# cv2.imshow('dst',dst)
# cv2.waitKey(0)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(14, 6), dpi=100) # Set the size and pixels of the drawing area
plt.subplot(121) # The first in a row and two columns
plt.imshow(img)
plt.subplot(122) # The second in a row and two columns
plt.imshow(dst)
plt.show()
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After running the above program, two pictures will be displayed in the jupyterLab control interface, as shown below.
[Original picture] [Skin whitening picture]
Bilateral filtering is a nonlinear filtering method. This method can only filter out low-frequency information better.
The code is as follows,
import cv2
import matplotlib.pyplot as plt
img = cv2.imread('yahboom.jpg',1)
#cv2.imshow('src',img)
dst = cv2.bilateralFilter(img,15,35,35)
# cv2.imshow('dst',dst)
# cv2.waitKey(0)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(14, 6), dpi=100) # Set the size and pixels of the drawing area
plt.subplot(121) # The first in a row and two columns
plt.imshow(img)
plt.subplot(122) # The second in a row and two columns
plt.imshow(dst)
plt.show()
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After running the above program, two pictures will be displayed in the jupyterLab control interface, as shown below.
[Original picture] [Skin whitening picture]